FD$^2$: A Dedicated Framework for Fine-Grained Dataset Distillation
📰 ArXiv cs.AI
FD$^2$ is a framework for fine-grained dataset distillation that improves efficiency and accuracy by leveraging detailed class information
Action Steps
- Decoupling the dataset distillation pipeline into pretraining, sample distillation, and soft-label generation
- Utilizing fine-grained class information to optimize sample distillation
- Generating soft labels that capture detailed class relationships
- Applying the FD$^2$ framework to various datasets and tasks to evaluate its effectiveness
Who Needs to Know This
Machine learning researchers and engineers on a team can benefit from FD$^2$ as it enables more efficient and effective dataset distillation, while data scientists can apply the framework to various applications
Key Insight
💡 FD$^2$ improves dataset distillation efficiency and accuracy by leveraging fine-grained class information
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🚀 FD$^2$: A dedicated framework for fine-grained dataset distillation! 💡
Key Takeaways
FD$^2$ is a framework for fine-grained dataset distillation that improves efficiency and accuracy by leveraging detailed class information
Full Article
Title: FD$^2$: A Dedicated Framework for Fine-Grained Dataset Distillation
Abstract:
arXiv:2603.25144v1 Announce Type: cross Abstract: Dataset distillation (DD) compresses a large training set into a small synthetic set, reducing storage and training cost, and has shown strong results on general benchmarks. Decoupled DD further improves efficiency by splitting the pipeline into pretraining, sample distillation, and soft-label generation. However, existing decoupled methods largely rely on coarse class-label supervision and optimize samples within each class in a nearly identical
Abstract:
arXiv:2603.25144v1 Announce Type: cross Abstract: Dataset distillation (DD) compresses a large training set into a small synthetic set, reducing storage and training cost, and has shown strong results on general benchmarks. Decoupled DD further improves efficiency by splitting the pipeline into pretraining, sample distillation, and soft-label generation. However, existing decoupled methods largely rely on coarse class-label supervision and optimize samples within each class in a nearly identical
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